Construction of a neural network model for small-scale shaded relief maps constrained by topographic feature lines
Traditional manual methods for generating shaded relief maps can effectively highlight major topographic structures but are time-consuming and require professional skills. Analytical shading methods are faster but often lead to maps overloaded with terrain details, obscuring key topographic features...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | Geocarto International |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/10106049.2025.2459099 |
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| _version_ | 1832582674741460992 |
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| author | Yue Wang Wenping Jiang Han Jiang Danfeng Dai Peiyang Ma Yuan Wang Zhizhi Wang |
| author_facet | Yue Wang Wenping Jiang Han Jiang Danfeng Dai Peiyang Ma Yuan Wang Zhizhi Wang |
| author_sort | Yue Wang |
| collection | DOAJ |
| description | Traditional manual methods for generating shaded relief maps can effectively highlight major topographic structures but are time-consuming and require professional skills. Analytical shading methods are faster but often lead to maps overloaded with terrain details, obscuring key topographic features, especially in Small-Scale Shaded Relief Maps (SSSR-Maps). This study focuses on the relief shading of alpine canyon terrain, introduces topographic feature lines (TFLs) as constraints, and constructs a neural network model based on Pix2pixHD, namely, TFLC-CGAN. Two generation methods, TFLC-CGAN-E and TFLC-CGAN-M, are proposed and compared. Experimental results show that TFLC-CGAN can generate SSSR-Maps with manual shading styles, simplifying terrain while preserving key features. TFLC-CGAN-E adapts better to sharply reduced TFL density, while TFLC-CGAN-M excels in feature preservation. Additionally, the relationships among digital elevation model resolution, TFL density, and the generated shaded relief map scales are explored. The proposed TFLC-CGAN offers an efficient solution for large-scale production of SSSR-Maps. |
| format | Article |
| id | doaj-art-3ed5589fdcfb4f5ca1e435c36938576b |
| institution | Kabale University |
| issn | 1010-6049 1752-0762 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geocarto International |
| spelling | doaj-art-3ed5589fdcfb4f5ca1e435c36938576b2025-01-29T12:16:41ZengTaylor & Francis GroupGeocarto International1010-60491752-07622025-12-0140110.1080/10106049.2025.2459099Construction of a neural network model for small-scale shaded relief maps constrained by topographic feature linesYue Wang0Wenping Jiang1Han Jiang2Danfeng Dai3Peiyang Ma4Yuan Wang5Zhizhi Wang6School of Resource and Environmental Sciences, Wuhan University, Wuhan, PR ChinaSchool of Resource and Environmental Sciences, Wuhan University, Wuhan, PR ChinaSchool of Engineering, Case Western Reserve University, Cleveland, USASchool of Resource and Environmental Sciences, Wuhan University, Wuhan, PR ChinaSchool of Geography and Information Engineering, China University of Geosciences, Wuhan, PR ChinaBig Data Center of Geospatial and Natural Resources of Qinghai Province, Xining, PR ChinaSchool of Computer Science, Hubei University of Technology, Wuhan, PR ChinaTraditional manual methods for generating shaded relief maps can effectively highlight major topographic structures but are time-consuming and require professional skills. Analytical shading methods are faster but often lead to maps overloaded with terrain details, obscuring key topographic features, especially in Small-Scale Shaded Relief Maps (SSSR-Maps). This study focuses on the relief shading of alpine canyon terrain, introduces topographic feature lines (TFLs) as constraints, and constructs a neural network model based on Pix2pixHD, namely, TFLC-CGAN. Two generation methods, TFLC-CGAN-E and TFLC-CGAN-M, are proposed and compared. Experimental results show that TFLC-CGAN can generate SSSR-Maps with manual shading styles, simplifying terrain while preserving key features. TFLC-CGAN-E adapts better to sharply reduced TFL density, while TFLC-CGAN-M excels in feature preservation. Additionally, the relationships among digital elevation model resolution, TFL density, and the generated shaded relief map scales are explored. The proposed TFLC-CGAN offers an efficient solution for large-scale production of SSSR-Maps.https://www.tandfonline.com/doi/10.1080/10106049.2025.2459099Small-scale shaded relief map (SSSR-map)topographic feature lines (TFLs)neural networkterrain simplification |
| spellingShingle | Yue Wang Wenping Jiang Han Jiang Danfeng Dai Peiyang Ma Yuan Wang Zhizhi Wang Construction of a neural network model for small-scale shaded relief maps constrained by topographic feature lines Geocarto International Small-scale shaded relief map (SSSR-map) topographic feature lines (TFLs) neural network terrain simplification |
| title | Construction of a neural network model for small-scale shaded relief maps constrained by topographic feature lines |
| title_full | Construction of a neural network model for small-scale shaded relief maps constrained by topographic feature lines |
| title_fullStr | Construction of a neural network model for small-scale shaded relief maps constrained by topographic feature lines |
| title_full_unstemmed | Construction of a neural network model for small-scale shaded relief maps constrained by topographic feature lines |
| title_short | Construction of a neural network model for small-scale shaded relief maps constrained by topographic feature lines |
| title_sort | construction of a neural network model for small scale shaded relief maps constrained by topographic feature lines |
| topic | Small-scale shaded relief map (SSSR-map) topographic feature lines (TFLs) neural network terrain simplification |
| url | https://www.tandfonline.com/doi/10.1080/10106049.2025.2459099 |
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